Instructions to use sanjay734/sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sanjay734/sentiment-analyzer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sanjay734/sentiment-analyzer")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sanjay734/sentiment-analyzer", device_map="auto")

PEFT
How to use sanjay734/sentiment-analyzer with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sanjay734/sentiment-analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sanjay734/sentiment-analyzer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sanjay734/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sanjay734/sentiment-analyzer

SGLang

How to use sanjay734/sentiment-analyzer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sanjay734/sentiment-analyzer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sanjay734/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sanjay734/sentiment-analyzer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sanjay734/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sanjay734/sentiment-analyzer with Docker Model Runner:
```
docker model run hf.co/sanjay734/sentiment-analyzer
```

sentiment-analyzer / tokenizer_config.json

sanjay734

Upload folder using huggingface_hub

621ed22 verified about 1 month ago

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518 Bytes

{
  "backend": "tokenizers",
  "bos_token": "<bos>",
  "clean_up_tokenization_spaces": false,
  "eos_token": "<eos>",
  "extra_special_tokens": [
    "<start_of_turn>",
    "<end_of_turn>"
  ],
  "is_local": false,
  "local_files_only": false,
  "mask_token": "<mask>",
  "model_max_length": 1000000000000000019884624838656,
  "pad_token": "<pad>",
  "sp_model_kwargs": {},
  "spaces_between_special_tokens": false,
  "tokenizer_class": "GemmaTokenizer",
  "unk_token": "<unk>",
  "use_default_system_prompt": false
}

	{
	"backend": "tokenizers",
	"bos_token": "<bos>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<eos>",
	"extra_special_tokens": [
	"<start_of_turn>",
	"<end_of_turn>"
	],
	"is_local": false,
	"local_files_only": false,
	"mask_token": "<mask>",
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "<pad>",
	"sp_model_kwargs": {},
	"spaces_between_special_tokens": false,
	"tokenizer_class": "GemmaTokenizer",
	"unk_token": "<unk>",
	"use_default_system_prompt": false
	}